Heterogeneous Networks (HetNets) are wireless communication networks with a multi-layered architecture of different cell sizes. In a HetNet, the overlaid small cells coexist in the same geographical area with macro cells potentially sharing the same spectrum with the macro cells. When all heterogeneous nodes share the same spectrum, two kinds of interference appear:                Cross-tier interference: The aggressor (e.g., a small cell) and the victim of interference (e.g., a user equipment (UE) currently served by the macro cell base station) belong to different tiers.        Intra-tier interference: The aggressor (e.g., a small cell) and the victim (e.g., a neighbouring small cell UE) belong to the same tier.        
In the HetNet, efficient spectrum utilisation in small cells should be employed in order to minimize interference to the macro cell eNodeB from small cell eNodeB. As a result, the small cell eNodeBs can harmoniously operate with the macro cell eNodeBs to deliver improved system throughput and quality of service compared to the network without small cell eNodeBs. To enable effective interference mitigation, the small cell eNodeBs should better be aware of the radio resource usage of other eNodeBs in the neighbourhood, especially macro cell eNodeBs, as illustrated in FIG. 1. FIG. 1 is a schematic diagram of a HetNet wireless communication system. A HetNet wireless communication network 1 includes multiple macro cell eNodeBs such as eNodeB 10. For simplicity of illustration only the macro cell eNodeB 10 is shown in FIG. 1. Within the radio service coverage of the macro cell eNodeB 10 the network operator can also deploy multiple small cells in order to enhance spectrum utilization of the HetNet communication system and also improve quality of service at the cell-edge of eNodeB 10. For example, there may be small cell eNodeBs 21, 22, 23, 24 and 25 deployed within the marco cell eNodeB 10. The small cell eNodeBs 21, 22, 23, 24 and 25 may be integrated with the macro cell eNodeB in the HetNet through one or more network devices (such as a network device 20) in a core network.
However, in a current Long-term Evolution (LTE) system there are very limited inter-cell or inter-tier negotiations. Even though LTE specifications define X2-interfaces, usually the X2-interfaces are deployed between eNodeBs from the same communication equipment vendors. Although X2-interfaces may be deployed between the macro cell eNodeBs and the neighbouring small cell eNodeBs, in some cases the backhaul may not always be available between different types of eNodeBs, or the backhaul may not meet the delay and bandwidth requirements of inter-cell/inter-tier coordination. Due to the lack of communication links to carry channel information or resource usage decisions between macro and small cells, interference management needs to be done autonomously at the small cell layer based on measurement of received signals from neighbouring cells. Autonomous interference management carried out at small cell eNodeBs can also avoid extra signalling and related operational cost for deploying X2-interfaces between the macro cell eNodeBs and the neighbouring small cell eNodeBs.
A possible solution to the HetNet system is self-organization of small cell eNodeBs and allocation of the radio resources of the small cells in an autonomous manner without involving a huge amount of cross-tier negotiations. To achieve this goal the small cells need to be equipped with a cognitive and learning capability to be aware of the interference situation and other parameters based on measurements of radio links.
For example, in an Orthogonal Frequency-Division Multiplexing (OFDM) wireless communication system, such as a LTE wireless communication supporting Third Generation Partnership Project (3GPP) communication standard, in order to optimise the transmission across the sub-bands appropriately, e.g., to enable a fast (per transmission time interval (TTI)) response on any change in interference or channel conditions, the small cells need to allow an advanced sniffer to detect neighbouring macro cell per physical radio resource block (PRB) usage by using one or more of following options: (i) commercial product, cognitive radio spectrum sensing unit to measure downlink (DL) received interference power (related to physical downlink shared channel (PDSCH)), or (ii) a sensing unit to decode DL transmission of the macro cell eNodeB where the sensing unit may be embedded in one or more small cells or installed along with the small cells.
In the solution of employing a sensing unit to decode DL transmission of the macro cell eNodeB (related to physical downlink control channel (PDCCH)), as PDCCH carries the downlink scheduling assignments and uplink scheduling grants, this solution seems a better source of very short term information in relation to what the macro cell eNodeB is transmitting or will be transmitting in DL.
As can be understood by a person of ordinary skill in the field of LTE communication systems, PDCCH information data are mapped on aggregations of Common Control Elements (CCE). For example, one CCE contains 72 bits of data, and there are 4 aggregations allowed: 1, 2, 4 or 8 CCEs as defined in 3GPP Technical Standard. The last 16 cyclic redundancy check (CRC) bits of PDCCH are scrambled by a temporary identifier assigned by the eNodeB to the UE, such as cell-radio network temporary identifier (C-RNTI). The information bits appended with scrambled CRC bits are then convolutionally encoded by the tail-biting convolutional encoder at the eNodeB.
Conventionally, when any given UE wants to decode its PDCCH allocation data it must first convolutionally decode encoded data. In a worst case scenario the UE needs to attempt all four combinations of aggregations within its UE-specific search space as well as common search space. Given that the Viterbi algorithm is used for convolutional decoding, the one (out of four) with the highest metric and ending up in the same state indicates the correct PDCCH sequence. Following the convolutional decoding, the UE descrambles the last 16 bits of the received sequence with its C-RNTI to obtain the CRC bits that were appended in the scrambling process at the eNodeB. If the CRC check is passed, the assumption is made by the UE that downlink control information (DCI) in PDCCH has been received by the UE correctly. Essentially, the CRC bits provide an additional layer of data verification for the UE.
Small cells in the proposed scenario have to recover radio resource allocations by the macro cell eNodeB for all UEs within radio service coverage of the marco cell eNodeB. However, the small cell does not possess knowledge of C-RNTIs used by the UE in the macro-cell. In order to obtain the radio resource allocation (map) of the macro cell, in theory, any given small cell has to decode all PDCCH messages for all UEs. Decoding all PDCCH messages for all UEs may be translated to convolutionally decoding PDCCH messages in the total search space and discard the CRC verification step. CRC verification seems impossible in the case where the sniffer has no knowledge of C-RNTI as the CRC section of the sequence is masked by exclusively-ORed with C-RNTI of the UE in the PDCCH messages. Nonetheless, without CRC check, the aggregation level that should be used by the UE can never be determined, and thus the UE cannot confirm that the blind decoding of the received PDCCH message was successful.
For example, published international patent application WO2014019155 A1 proposes a similar approach to acquire, at a given base station, uplink and downlink scheduling information from a neighbouring cell through blindly decoding PDCCH by different C-RNTI parameters for CRC check until a C-RNTI is found corresponding to the CRC. However, such an approach is very time consuming since there are currently 4,083 different C-RNTI parameters that need to be blind decoded. It is simply not feasible to obtain the correct C-RNTI by attempting more than 4,083 blind decoding on PDCCH sequence within a short duration, such as a TTI (equivalent to 10 millisecond (ms)). If taking into account different aggregation levels of CCEs in PDCCH, blind decoding using each C-RNTI may require attempting 44 combinations of DCIs at all possible aggregation levels in PDCCH, and the total number of blind decoding using each C-RNTI may, in a worst case scenario, approach 179,652 blind decoding calculations per TTI. WO2014019155 A1 provides no further details in relation to how to blindly decode PDCCH messages for acquiring uplink and downlink scheduling information from a neighbouring cell or more neighbouring cells within a TTI.
Against this background, there is a need for a method, an apparatus or a system to efficiently and accurately acquire scheduling information assigned to one or more user devices within a radio service coverage of a neighbouring cell eNodeB.